1. Field of the Invention
This invention relates generally to a system and method for determining the future state of health of components and sub-systems in a complex system and, more particularly, to a system and method for determining the future state of health of components and sub-systems in a complex system that uses system design fault models and component aging models.
2. Discussion of the Related Art
Modern vehicles are complex electrical and mechanical systems that employ many components, devices, modules, sub-systems, etc. that pass electrical information between and among each other using sophisticated algorithms and data buses. As with anything, these types of devices and algorithms are susceptible to errors, failures and faults that affect the operation of the vehicle. When such errors and faults occur, often the affected device or component will issue a fault code, such as diagnostic trouble code (DTC), that is received by one or more system controllers identifying the fault, or some ancillary fault with an integrated component. These DTCs can be analyzed by service technicians and engineers to identify problems and/or make system corrections and upgrades.
Vehicle fault models that define the faults that could occur in a vehicle and the remedies available for those faults are becoming more prevalent in the industry. One of the most simplistic representations of a fault model is a two-dimensional matrix where the rows of the matrix capture the failure modes that could occur on the vehicle and the columns of the matrix identify the symptoms that the vehicle may experience for the failure modes. The fault model captures the causal dependencies among the failure modes and symptoms. The various symptoms could be information that is recorded during operation of the vehicle, or information that is occurring while the vehicle is being serviced. Thus, by placing an indicator at the cross-section between a particular failure mode and the symptoms that the vehicle would undergo for those failure modes in the fault model, service personnel can identify what service operation needs to be performed based on the symptoms that are occurring to correct a particular failure.
As discussed above, fault modeling has been employed to diagnose component and sub-system problems in complex vehicle systems. Diagnostic modeling includes determining the root cause of a problem that has already occurred. Known fault modeling methods for diagnosing component and sub-system faults may use Bayesian networks, dynamic Bayesian networks, hidden Markov models, fuzzy logic, belief networks, Petri net, etc. However, fault modeling has heretofore not been used to provide the prognosis for the future state of health of complex systems. As a system becomes more complex the ability to provide system health prognosis becomes more difficult, but just as necessary.
It is also known in the art to employ component aging models, such as Arrhenius equation models, Paris equation models, etc., to estimate the age of a component in a system. However, such aging models have not been extended to systems, especially complex systems.